loss_fun parameter validationf for entropic GW functions

ot/gromov/_bregman.py

@@ -81,7 +81,7 @@ def entropic_gromov_wasserstein(
     q : array-like, shape (nt,), optional
         Distribution in the target space.
         If let to its default value None, uniform distribution is taken.
-    loss_fun :  string, optional
+    loss_fun : string, optional (default='square_loss')
         Loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float, optional
         Regularization term >0
@@ -92,8 +92,8 @@ def entropic_gromov_wasserstein(
     G0: array-like, shape (ns,nt), optional
         If None the initial transport plan of the solver is pq^T.
         Otherwise G0 will be used as initial transport of the solver. G0 is not
-        required to satisfy marginal constraints but we strongly recommand it
-        to correcly estimate the GW distance.
+        required to satisfy marginal constraints but we strongly recommend it
+        to correctly estimate the GW distance.
     max_iter : int, optional
         Max number of iterations
     tol : float, optional
@@ -135,6 +135,9 @@ def entropic_gromov_wasserstein(
     if solver not in ['PGD', 'PPA']:
         raise ValueError("Unknown solver '%s'. Pick one in ['PGD', 'PPA']." % solver)
 
+    if loss_fun not in ('square_loss', 'kl_loss'):
+        raise ValueError(f"Unknown `loss_fun='{loss_fun}'`. Use one of: {'square_loss', 'kl_loss'}.")
+
     C1, C2 = list_to_array(C1, C2)
     arr = [C1, C2]
     if p is not None:
@@ -280,7 +283,7 @@ def entropic_gromov_wasserstein2(
     q : array-like, shape (nt,), optional
         Distribution in the target space.
         If let to its default value None, uniform distribution is taken.
-    loss_fun :  string, optional
+    loss_fun : string, optional (default='square_loss')
         Loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float, optional
         Regularization term >0
@@ -373,8 +376,8 @@ def entropic_gromov_barycenters(
     lambdas : list of float, optional
         List of the `S` spaces' weights.
         If let to its default value None, uniform weights are taken.
-    loss_fun : callable, optional
-        tensor-matrix multiplication function based on specific loss function
+    loss_fun : string, optional (default='square_loss')
+        Loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float, optional
         Regularization term >0
     symmetric : bool, optional.
@@ -411,6 +414,9 @@ def entropic_gromov_barycenters(
         "Gromov-Wasserstein averaging of kernel and distance matrices."
         International Conference on Machine Learning (ICML). 2016.
     """
+    if loss_fun not in ('square_loss', 'kl_loss'):
+        raise ValueError(f"Unknown `loss_fun='{loss_fun}'`. Use one of: {'square_loss', 'kl_loss'}.")
+
     Cs = list_to_array(*Cs)
     arr = [*Cs]
     if ps is not None:
@@ -459,7 +465,6 @@ def entropic_gromov_barycenters(
 
         if loss_fun == 'square_loss':
             C = update_square_loss(p, lambdas, T, Cs)
-
         elif loss_fun == 'kl_loss':
             C = update_kl_loss(p, lambdas, T, Cs)
 
@@ -550,21 +555,21 @@ def entropic_fused_gromov_wasserstein(
     q : array-like, shape (nt,), optional
         Distribution in the target space.
         If let to its default value None, uniform distribution is taken.
-    loss_fun :  string, optional
+    loss_fun : string, optional (default='square_loss')
         Loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float, optional
         Regularization term >0
     symmetric : bool, optional
         Either C1 and C2 are to be assumed symmetric or not.
         If let to its default None value, a symmetry test will be conducted.
-        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric).
+        Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
     alpha : float, optional
         Trade-off parameter (0 < alpha < 1)
     G0: array-like, shape (ns,nt), optional
         If None the initial transport plan of the solver is pq^T.
         Otherwise G0 will be used as initial transport of the solver. G0 is not
-        required to satisfy marginal constraints but we strongly recommand it
-        to correcly estimate the GW distance.
+        required to satisfy marginal constraints but we strongly recommend it
+        to correctly estimate the GW distance.
     max_iter : int, optional
         Max number of iterations
     tol : float, optional
@@ -611,6 +616,9 @@ def entropic_fused_gromov_wasserstein(
     if solver not in ['PGD', 'PPA']:
         raise ValueError("Unknown solver '%s'. Pick one in ['PGD', 'PPA']." % solver)
 
+    if loss_fun not in ('square_loss', 'kl_loss'):
+        raise ValueError(f"Unknown `loss_fun='{loss_fun}'`. Use one of: {'square_loss', 'kl_loss'}.")
+
     M, C1, C2 = list_to_array(M, C1, C2)
     arr = [M, C1, C2]
     if p is not None:
@@ -762,7 +770,7 @@ def entropic_fused_gromov_wasserstein2(
     q : array-like, shape (nt,), optional
         Distribution in the target space.
         If let to its default value None, uniform distribution is taken.
-    loss_fun :  string, optional
+    loss_fun : string, optional (default='square_loss')
         Loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float, optional
         Regularization term >0
@@ -775,8 +783,8 @@ def entropic_fused_gromov_wasserstein2(
     G0: array-like, shape (ns,nt), optional
         If None the initial transport plan of the solver is pq^T.
         Otherwise G0 will be used as initial transport of the solver. G0 is not
-        required to satisfy marginal constraints but we strongly recommand it
-        to correcly estimate the GW distance.
+        required to satisfy marginal constraints but we strongly recommend it
+        to correctly estimate the GW distance.
     max_iter : int, optional
         Max number of iterations
     tol : float, optional
@@ -857,8 +865,8 @@ def entropic_fused_gromov_barycenters(
     lambdas : list of float, optional
         List of the `S` spaces' weights.
         If let to its default value None, uniform weights are taken.
-    loss_fun : callable, optional
-        tensor-matrix multiplication function based on specific loss function
+    loss_fun : string, optional (default='square_loss')
+        Loss function used for the solver either 'square_loss' or 'kl_loss'
     epsilon : float, optional
         Regularization term >0
     symmetric : bool, optional.
@@ -907,6 +915,9 @@ def entropic_fused_gromov_barycenters(
         "Optimal Transport for structured data with application on graphs"
         International Conference on Machine Learning (ICML). 2019.
     """
+    if loss_fun not in ('square_loss', 'kl_loss'):
+        raise ValueError(f"Unknown `loss_fun='{loss_fun}'`. Use one of: {'square_loss', 'kl_loss'}.")
+
     Cs = list_to_array(*Cs)
     Ys = list_to_array(*Ys)
     arr = [*Cs, *Ys]
@@ -977,7 +988,6 @@ def entropic_fused_gromov_barycenters(
 
         if loss_fun == 'square_loss':
             C = update_square_loss(p, lambdas, T, Cs)
-
         elif loss_fun == 'kl_loss':
             C = update_kl_loss(p, lambdas, T, Cs)
 

test/test_gromov.py

@@ -311,7 +311,12 @@ def test_gw_helper_validation(loss_fun):
 
 @pytest.skip_backend("jax", reason="test very slow with jax backend")
 @pytest.skip_backend("tf", reason="test very slow with tf backend")
-def test_entropic_gromov(nx):
+@pytest.mark.parametrize('loss_fun', [
+    'square_loss',
+    'kl_loss',
+    pytest.param('unknown_loss', marks=pytest.mark.xfail(raises=ValueError)),
+])
+def test_entropic_gromov(nx, loss_fun):
     n_samples = 10  # nb samples
 
     mu_s = np.array([0, 0])
@@ -333,10 +338,10 @@ def test_entropic_gromov(nx):
     C1b, C2b, pb, qb, G0b = nx.from_numpy(C1, C2, p, q, G0)
 
     G, log = ot.gromov.entropic_gromov_wasserstein(
-        C1, C2, None, q, 'square_loss', symmetric=None, G0=G0,
+        C1, C2, None, q, loss_fun, symmetric=None, G0=G0,
         epsilon=1e-2, max_iter=10, verbose=True, log=True)
     Gb = nx.to_numpy(ot.gromov.entropic_gromov_wasserstein(
-        C1b, C2b, pb, None, 'square_loss', symmetric=True, G0=None,
+        C1b, C2b, pb, None, loss_fun, symmetric=True, G0=None,
         epsilon=1e-2, max_iter=10, verbose=True, log=False
     ))
 
@@ -347,11 +352,40 @@ def test_entropic_gromov(nx):
     np.testing.assert_allclose(
         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov
 
+
+@pytest.skip_backend("jax", reason="test very slow with jax backend")
+@pytest.skip_backend("tf", reason="test very slow with tf backend")
+@pytest.mark.parametrize('loss_fun', [
+    'square_loss',
+    'kl_loss',
+    pytest.param('unknown_loss', marks=pytest.mark.xfail(raises=ValueError)),
+])
+def test_entropic_gromov2(nx, loss_fun):
+    n_samples = 10  # nb samples
+
+    mu_s = np.array([0, 0])
+    cov_s = np.array([[1, 0], [0, 1]])
+
+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)
+
+    xt = xs[::-1].copy()
+
+    p = ot.unif(n_samples)
+    q = ot.unif(n_samples)
+    G0 = p[:, None] * q[None, :]
+    C1 = ot.dist(xs, xs)
+    C2 = ot.dist(xt, xt)
+
+    C1 /= C1.max()
+    C2 /= C2.max()
+
+    C1b, C2b, pb, qb, G0b = nx.from_numpy(C1, C2, p, q, G0)
+
     gw, log = ot.gromov.entropic_gromov_wasserstein2(
-        C1, C2, p, None, 'kl_loss', symmetric=True, G0=None,
+        C1, C2, p, None, loss_fun, symmetric=True, G0=None,
         max_iter=10, epsilon=1e-2, log=True)
     gwb, logb = ot.gromov.entropic_gromov_wasserstein2(
-        C1b, C2b, None, qb, 'kl_loss', symmetric=None, G0=G0b,
+        C1b, C2b, None, qb, loss_fun, symmetric=None, G0=G0b,
         max_iter=10, epsilon=1e-2, log=True)
     gwb = nx.to_numpy(gwb)